The present invention relates to image compression and, more particularly, a method of distortion adaptive frequency weighting for image compression.
Communication systems are used to transmit information generated by a source to some destination for consumption by an information sink. Source coding or data compression is a process of encoding the output of an information source into a format that reduces the quantity of data that must be transmitted or stored by the communication system. Data compression may be accomplished by lossless or lossy methods or a combination thereof. The objective of lossy compression is the elimination of the more redundant and irrelevant data in the information obtained from the source.
Video includes temporally redundant data in the similarities between the successive images of the video sequence and spatially redundant data in the similarities between pixels and patterns of pixels within the individual images of the sequence. Temporally redundant data may be reduced by identifying similarities between successive images and using these similarities and an earlier image to predict later images. Spatially redundant data is characterized by the similarity of pixels in flat areas or the presence of dominant frequencies in patterned areas of an image. Reduction of spatially redundant data is typically accomplished by the steps of transformation, quantization, and entropy coding of the image data. Transformation converts the original image signal into a plurality of transform coefficients which more efficiently represent the image for the subsequent quantization and entropy coding phases. Following transformation, the transform coefficients are mapped to a limited number of possible data values or quantized. The quantized data is further compressed by lossless entropy coding where shorter codes are used to describe more frequently occurring data symbols or sequences of symbols.
Quantization is a lossy process and a significant part of the overall compression of video data is the result of discarding data during quantization. The underlying basis for lossy compression is the assumption that some of the data is irrelevant and can be discarded without unduly effecting the perceived quality of the reconstructed image. In fact, due to the characteristics of the human visual system (HVS) a large portion of the data representing visual information is irrelevant to the visual system and can be discarded without exceeding the threshold of human visual perception. As the lossiness of the compression process is increased, more data are discarded reducing the data to be stored or transmitted but increasing the differences between the original image and the image after compression or the distortion of the image and the likelihood that the distortion will be visually perceptible and objectionable.
One measure of human visual perception is contrast sensitivity which expresses the limits of visibility of low contrast patterns. Contrast is the difference in intensity between two points of a visual pattern. Visual sensitivity to contrast is affected by the viewing distance, the illumination level, and, because of the limited number of photoreceptors in the eye, the spatial frequency of the contrasting pattern. Contrast sensitivity is established by increasing the amplitude of a test frequency basis function until the contrast reaches a “just noticeable difference” (JND) where humans can detect the signal under the specific viewing conditions. As illustrated in FIG. 1, a plot of the JND produces a contrast sensitivity function (CSF) 10 expressing human visual contrast sensitivity as a function of the spatial frequency of the visual stimulus for specific viewing conditions. Since human eyes are less sensitive to high frequency patterns, high frequency components of an image can be quantized more coarsely than low frequency components or discarded with less impact on human perception of the image.
Frequency weighting is a commonly used technique for visually optimizing data compression in both discrete cosine transform (DCT) and wavelet-based image compression systems to take advantage of the contrast sensitivity function (CSF). CSF frequency weighting has been used to scale the coefficients produced by transformation before application of uniform quantization. On the other hand, CSF frequency weighting may be applied to produce quantization steps of varying sizes which are applied to the different frequency bands making up the image. In a third technique, CSF frequency weighting may be used to control the order in which sub-bitstreams originating from different frequency bands are assembled into a final embedded bitstream. The CSF has been assumed to be single valued for specific viewing conditions. However, the CSF is determined under near visually lossless conditions and observation indicates that the contrast sensitivity of the human visual system is affected by image distortion which is, in turn, inversely impacted by data compression efficiency. What is desired therefore, is a method of improved visual optimization of image data source coding useful at the low data rates of systems employing high efficiency data compression.